This article introduced the use of probabilistic neural networks (PNN) to quantify uncertainties within a surrogate model for fluid flow reconstruction and forecasting applications.
The advantage against the conventional linear method was first demonstrated with a shallow water equation.
We then assessed the framework on the wake estimation of a NACA0012 airfoil with a Gurney flap and the NOAA sea surface temperature data set.
The present PNN showed promising potential as a reduced-order surrogate with uncertainty estimates of the targets conditioned on the inputs and the training data.
Although not shown here, the confidence intervals obtained by the present modeling strategy may also be utilized additional sensor placements in fluid flow data recovery tasks [1]}.
These observations enable us to expect various extensions of PNN to a wide spread of engineering applications.
